by Coinstrooper Crypto Articles
Quick Take
  • The world’s fastest supercomputer, LineShine in Shenzhen, draws 42.2 million watts.
  • That gap has become the internet’s favorite argument about AI energy use, and most of it is wrong.
  • However, the numbers circulating on social media trace back to a single paper.
  • The most striking one has been misattributed for three years.

What Happened

The human brain runs on roughly 20 watts. The world’s fastest supercomputer, LineShine in Shenzhen, draws 42.2 million watts. That gap has become the internet’s favorite argument about AI energy use, and most of it is wrong.

The comparison itself holds up. However, the numbers circulating on social media trace back to a single paper. The most striking one has been misattributed for three years.

AI Energy Use: What 20 Watts Actually Buys

Market Context

The 20-watt figure rests on decades of metabolic measurement. The brain accounts for about 2% of body weight and roughly 20% of resting oxygen consumption.

Neuron counts are shakier than they appear. The widely quoted 86 billion rests on four male brains and is currently under dispute in the journal Brain.

Why It Matters

Viral posts often use 12 watts rather than 20. That figure appears in a 2023 paper in Frontiers in Artificial Intelligence, stated without any citation at all.

The same paper produced the number everyone shares. Its authors estimated that digitally recreating a human brain would draw 2.7 billion watts.

Details

That estimate came from extrapolating a 10-million-neuron simulation to mouse scale, then multiplying by a thousand.

The paper also states that the simulation ran about 30,000 times slower than biology. Social posts drop that detail. Secondary sources then credit the figure to the Blue Brain Project, which never published it.

Reliable numbers do exist elsewhere. Epoch AI estimated a typical ChatGPT query at 0.3 watt-hours in early 2025. A peer-reviewed study in Joule later landed on 0.31.

Two independent methods agreeing that closely is unusual. However, the figure changes sharply with workload, and reasoning models that produce longer answers can cost several times as much.

What Biology Does Differently, and What Silicon Copied

Cortical activity is sparse. Average firing rates are below 1 Hz, and energy follows change rather than clock cycles.

Modern AI reached the same conclusion independently. Kimi K2 activates 32.6 billion of its 1.04 trillion parameters per token, close to 3.1%.

That ratio is falling fast. Mixtral used roughly 28% of its parameters in 2023, while DeepSeek-V3 now uses 5.5%.

Biology also computes at low precision. Nothing inside a neuron resolves to 32 bits.

Chipmakers followed the same path. DeepSeek trained a 671-billion-parameter model in eight-bit precision. NVIDIA has since pretrained a 12-billion-parameter model in four-bit.

The third difference is the largest and the least copied. Brains hold memory and computation in the same physical place.

Digital machines separate them. Stanford’s Mark Horowitz showed the cost of that split. Fetching an operand from memory can consume hundreds of times more energy than the arithmetic itself.

The Brain-Shaped Chips That Never Arrived

Hardware built explicitly to imitate neurons has struggled. No neuromorphic or analog system has trained or run a frontier model in production.

Intel’s Hala Point packs 1.15 billion artificial neurons across 1,152 chips. It remains a research prototype installed at Sandia National Laboratories. Mike Davies, director of Intel’s Neuromorphic Computing Lab, speaking to The Register in 2024, said:

“We’re not mapping any LLM to Hala Point at this time. We don’t know how to do that.”

The commercial picture is thinner still. BrainChip is the sector’s flagship listed company. It reported $700,000 in customer receipts against $5.3 million of operating outflow last March quarter.